RRAM Random Number Generator Based on Train of Pulses

Abstract

In this paper, the modulation of the conductance levels of resistive random access memory (RRAM) devices is used for the generation of random numbers by applying a train of RESET pulses. The influence of the pulse amplitude and width on the device resistance is also analyzed. For each pulse characteristic, the number of pulses required to drive the device to a particular resistance threshold is variable, and it is exploited to extract random numbers. Based on this behavior, a random number generator (RNG) circuit is proposed. To assess the performance of the circuit, the National Institute of Standards and Technology (NIST) randomness tests are applied to evaluate the randomness of the bitstreams obtained. The experimental results show that four random bits are simultaneously obtained, passing all the applied tests without the need for post-processing. The presented method provides a new strategy to generate random numbers based on RRAMs for hardware security applications.

Hardware implementation of a true random number generator integrating a hexagonal boron nitride memristor with a commercial microcontroller

“…However, those studies only constructed and characterized single devices, and the circuital part was simulated or modelled. References [23][24][25][26][27][28][29] have shown functional verifications based on complex experimental setups that involve laboratory characterization equipment and commercial programming tools, i.e., they are not stand-alone solutions. References 27,28,30 went farther and implemented parts of the circuit with components-of-the-shelf mounted on a protoboard, but the throughput was only 6 Kilobit/s, 30 the power overhead of the entropy source was too high in the low resistance state 31 , and required very high operating voltages 26 .…”

“…Ref. [23][24][25][26][27][28][29] have shown functional verifications based on complex experimental setups that involve laboratory characterization equipment and commercial programming tools, i.e., they are not stand-alone solutions. Ref.…”

“…27, 28 and 30 went farther and implemented parts of the circuit with components-of-the-shelf mounted on a protoboard, but the throughput was only 6 kilobit per s, 30 the power overhead of the entropy source was too high in the low resistance state, 31 and it required very high operating voltages. [26][27][28][29][30][31][32][33][34][35][36][37][38][39] proposed that the random telegraph noise (RTN) current signals produced by memristors (i.e., stochastic current fluctuations between two or more levels when a low and constant voltage is applied) could be used as an entropy source in TRNG circuits. The advantage of this approach is that the resis-tance change is related to electron trapping and de-trapping at the defects of the insulator, which reduces the energy consumption and slows down the degradation of the MIM nanocell.…”

Thermal Characterization of Conductive Filaments in Unipolar Resistive Memories

“…Another great field of development is linked to neuromorphic engineering [ 2 , 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 ]. In addition, these emerging devices are interesting for hardware cryptography in the context of the Internet of Things ecosystem [ 17 , 18 , 19 , 20 ]. Moreover, these devices, and other memristive devices [ 21 ], can be employed as radio-frequency switches for mobile communications [ 1 ].…”

Hardware Security Primitives using Passive RRAM Crossbar Array: Novel TRNG and PUF Designs

“…Although various CMOS-based designs have been implemented, they have limitations as they only provide limited security-specific properties. The emerging technologies, especially RRAM, have shown great potential due to high density, low power operation, and new randomness sources [19]. The major contributions of this work are as follows:…”